例谈物理核心素养视角下培养学生策略性学习的实践研究

文章结合自身教学实践,针对在物理核心素养背景下,从"借助项目设计,培养学习习惯,促进学生‘策略性学习’反应程序化""借助过程优化,积累学习方法,促进学生‘策略性学习’研究深度化""借助物理与生活互动,提高学生对学习理解,促进学生‘策略性学习’运用实践化"3个途径来提升学生思维水平,发展学生思维品质,进而提高学生物理核心素养.     

早期糖尿病风险预测模型的比较研究

糖尿病是一种比较常见的慢性疾病,并且存在较长的无症状阶段.本文主要介绍了机器学习中的5种分类算法,分别是朴素贝叶斯、支持向量机、逻辑回归、决策树和集成分类器Random Forest,并在Weka数据挖掘平台上,对糖尿病数据进行挖掘分析,根据混淆矩阵、Kappa系数、ROC曲线、均方根误差以及相对绝对误差这几个性能指标...     

Adversarial Machine Learning for NextG Covert Communications Using Multiple Antennas

This paper studies the privacy of wireless communications from an eavesdropper that employs a deep learning (DL) classifier to detect transmissions of interest. There exists one transmitter that transmits to its receiver in the presence of an eavesdropper. In the meantime, a cooperative jammer (CJ) with multiple antennas transmits carefully crafted adversarial perturbations over the air to fool the eavesdropper into classifying the received superposition of signals as noise. While generating the adversarial perturbation at the CJ, multiple antennas are utilized to improve the attack performance in terms of fooling the eavesdropper. Two main points are considered while exploiting the multiple antennas at the adversary, namely the power allocation among antennas and the utilization of channel diversity. To limit the impact on the bit error rate (BER) at the receiver, the CJ puts an upper bound on the strength of the perturbation signal. Performance results show that this adversarial perturbation causes the eavesdropper to misclassify the received signals as noise with a high probability while increasing the BER at the legitimate receiver only slightly. Furthermore, the adversarial perturbation is shown to become more effective when multiple antennas are utilized.     

Multivariate Time Series Imputation: An Approach Based on Dictionary Learning

The problem addressed by dictionary learning (DL) is the representation of data as a sparse linear combination of columns of a matrix called dictionary. Both the dictionary and the sparse representations are learned from the data. We show how DL can be employed in the imputation of multivariate time series. We use a structured dictionary, which is comprised of one block for each time series and a common block for all the time series. The size of each block and the sparsity level of the representation are selected by using information theoretic criteria. The objective function used in learning is designed to minimize either the sum of the squared errors or the sum of the magnitudes of the errors. We propose dimensionality reduction techniques for the case of high-dimensional time series. For demonstrating how the new algorithms can be used in practical applications, we conduct a large set of experiments on five real-life data sets. The missing data (MD) are simulated according to various scenarios where both the percentage of MD and the length of the sequences of MD are considered. This allows us to identify the situations in which the novel DL-based methods are superior to the existing methods.     

Stochastic Control for Bayesian Neural Network Training

In this paper, we propose to leverage the Bayesian uncertainty information encoded in parameter distributions to inform the learning procedure for Bayesian models. We derive a first principle stochastic differential equation for the training dynamics of the mean and uncertainty parameter in the variational distributions. On the basis of the derived Bayesian stochastic differential equation, we apply the methodology of stochastic optimal control on the variational parameters to obtain individually controlled learning rates. We show that the resulting optimizer, StochControlSGD, is significantly more robust to large learning rates and can adaptively and individually control the learning rates of the variational parameters. The evolution of the control suggests separate and distinct dynamical behaviours in the training regimes for the mean and uncertainty parameters in Bayesian neural networks.     

Systems thinking approaches for international green chemistry education

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基于深度学习的场景文字检测综述

近年来，基于深度学习的场景文字检测技术取得重要进展.本文综述了该技术在2014~2018年间的最新工作，将其分为传统区域建议方法、文字建议网络方法、基于分割的方法以及文字建议网络与分割的混合方法，并对各类方法的优劣进行分析.本文还展望了未来发展趋势，指出未来研究热点.     

Fusing Stretchable Sensing Technology with Machine Learning for Human–Machine Interfaces

Sensors and algorithms are two fundamental elements to construct intelligent systems. The recent progress in machine learning (ML) has produced great advancements in intelligent systems, owing to the powerful data analysis capability of ML algorithms. However, the performance of most systems is still hindered by sensing techniques that typically rely on rigid and bulky sensor devices, which cannot conform to irregularly curved and dynamic surfaces for high-quality data acquisition. Skin-like stretchable sensing technology with unique characteristics, such as high conformability, low modulus, and light weight, has been recently developed to solve this issue. Here, the recent progress in the fusion of emerging stretchable electronics and ML technology, for bioelectrical signal recognition, tactile perception, and multimodal integration is summarized, and the challenges and future developments are further discussed. These efforts aim to accelerate various perception and reasoning tasks for advanced intelligent applications, such as human–machine interfaces, healthcare, and robotics.     

一种基于多任务学习的科学文献推荐算法

传统推荐算法通过主题模型或者词语向量化的平均值对文本内容进行映射。针对现有方法不能充分利用文本信息或忽略词序信息这一问题,文中面向科学文献,提出了一种多任务学习推荐方法。该方法基于多任务学习框架,设计编码器并搭建了GL模型。该模型被训练为内容推荐与文本元数据预测的组合,可改善传统协同过滤的稀疏性问题,使得协同过滤模型正则化。最后,分别在公开数据集与私有数据集上进行了评估测试,结果表明所提方法性能优于现有的经典方法。     

Mutual Support of Ligand- and Structure-Based Approaches—To What Extent We Can Optimize the Power of Predictive Model? Case Study of Opioid Receptors

The process of modern drug design would not exist in the current form without computational methods. They are part of every stage of the drug design pipeline, supporting the search and optimization of new bioactive substances. Nevertheless, despite the great help that is offered by in silico strategies, the power of computational methods strongly depends on the input data supplied at the stage of the predictive model construction. The studies on the efficiency of the computational protocols most often focus on global efficiency. They use general parameters that refer to the whole dataset, such as accuracy, precision, mean squared error, etc. In the study, we examined machine learning predictions obtained for opioid receptors (mu, kappa, delta) and focused on cases for which the predictions were the most accurate and the least accurate. Moreover, by using docking, we tried to explain prediction errors. We attempted to develop a rule of thumb, which can help in the prediction of compound activity towards opioid receptors via docking, especially those that have been incorrectly predicted by machine learning. We found out that although the combination of ligand- and structure-based path can be beneficial for the prediction accuracy, there still remain cases that cannot be reliably predicted by any available modeling method. In addition to challenging ligand- and structure-based predictions, we also examined the role of the application of machine-learning methods in comparison to simple statistical methods for both standard ligand-based representations (molecular fingerprints) and interaction fingerprints. All approaches were confronted in both classification (where compounds were assigned to the group of active and inactive group constructed on the basis of K_i values) and regression (where exact K_i value was predicted) experiments.